8  Zooarchaeology

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8.1 Case studies

The following map shows the sites under investigation, divided by chronology. Please select the desired chronology (or chronologies) from the legend on the right. Legend: R = Roman, LR = Late Roman, EMA = Early Middle Ages, Ma = 11th c. onwards

The faunal dataset used in this study is both extensive and diverse, containing over 466 records. While NISP is a useful proxy for historical animal farming, it is not without its limitations, as previously discussed in the methods section. What is crucial to emphasize here, however, is the presence of overdispersion within the data, which necessitates more sophisticated and nuanced approaches than simply calculating overall means for each animal species. Overdispersion is a common occurrence when analyzing datasets of this nature, given the unique factors at play in each context, including historical and depositional influences. Nevertheless, data modeling requires simplification and causal reasoning, and the best approach is to start with straightforward models that can account for overdispersion and generate credible distributions. To this end, Bayesian hierarchical models were developed for each chronology, context type, macroregion, and geography. As a further step, an additional analysis was conducted that solely focused on altitude and chronology as predictors. By examining the specific contributions of altitude and chronology in predicting animal farming/consumption patterns, this analysis provides valuable insights that complement the earlier models. These findings underscore the importance of considering multiple factors when studying the probability of occurrence of farmed and wild animals in historical contexts. They also demonstrate the potential benefits of simplified models to focus on key predictors. Moreover, several attempts were made to create a coherent understanding of the likelihood of economically valuable animals occurring during the first millennium, in order to provide a more comprehensive perspective on the role of animal farming in shaping historical societies.

8.2 Data exploration

As previously mentioned, the faunal dataset used in this study exhibits overdispersion, which is a common issue when analysing datasets of this type. The term ‘overdispersion’ refers to the presence of greater variability in the data than expected based on a normal curve (Figure 8.1). In this section, I will present the distribution of the animals of interest to provide a visual representation of the dispersion within the dataset. By examining the distribution curves, we can better understand the variability that exist within the dataset, and can use this information to develop more accurate models. In Figure 8.2, it is evident that the distribution of animal remains in the faunal dataset is not symmetrical and does not conform to a normal curve. This non-normal distribution indicates that the standard measures of central tendency, such as mean and median, may not accurately capture the overall distribution of the data. Traditional statistical measures, such as measures of central tendency and dispersion, are commonly used in frequentist approaches to analyse data. However, these measures may not be appropriate for analysing the complex patterns of animal farming and consumption in the faunal dataset due to the presence of overdispersion. As an alternative, Bayesian multilevel models can account for overdispersion by incorporating appropriate probability distributions, such as the betabinomial distribution.

Figure 8.1: Probability density curves of three simulated normal distributions, representing underdispersed (sd = 0.5), normal (sd = 1), and overdispersed (sd = 2) data.

Figure 8.2: Distribution of animal remains in the dataset, displayed both by NISP raw value and NISP proportions.

8.3 Chronology

8.4 Context type

8.4.1 Pigs

Question: Is the animal X - let’s say Pigs - more strongly associated to a particular settlement type during a certain chronology?

Expectation 1: Pigs % should be higher in urban and fortified settlements, as they are animals which are only used for meat and can sustain large populations or the military.

Expectation 2: Possibly pigs would increase in villas in the Late Roman age, as their production shifts to a more extensive agriculture (Source: Historical literature).

Expectation 3: If urban density decreases during the late Roman and early Medieval phase, but increases in the Medieval age, do the pigs % follow similar trends?

To estimate the animals’ occurrences probability in each chronology and context, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[TCid]}\) as the model will provide estimates for each context type and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and context type.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[TCid]} \]

\[ \alpha_{[TCid]} \sim Normal(0,1.5) \]

\[ \phi_{[TCid]} \sim Exponential(1) + 2 \]

8.4.2 Cattle

8.4.3 Caprine

Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

8.4.4 Edible W. Mammals

8.4.5 Community plot

8.5 Macroregion

To estimate the animals’ occurrences probability in each chronology and macroregion, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[REGid]}\) as the model will provide estimates for each macroregion and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and macroregion.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[REGid]} \]

\[ \alpha_{[REGid]} \sim Normal(0,1.5) \]

\[ \phi_{[REGid]} \sim Exponential(1)+2 \]

8.5.1 Pigs

8.5.2 Cattle

8.5.3 Caprine

8.5.4 Edible W. Mammals

8.6 Geography

Animals distributions can vary across different geographical features. This research has considered plain, coast, hill and mountains as the most common geographical features in the Italian peninsula. Archaeological excavations where zooarchaeological remains have been analysed are located at low altitudes. Although as expected there are more mountain sites in Northern Italy, sampled sites are evenly placed on plains, coastlands and hills across the three Italian macroregions.

Figure 8.4: Distribution of sites on different geographical features.

To estimate the animals’ occurrences probability in each chronology and geography, I used a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is an intercept-only model, where the intercept \(\alpha\) carries an interaction index \({[GEOid]}\) as the model will provide estimates for each geography and chronology under examination. The \(\phi\) parameter indicates the precision in the Beta distribution, modelled by chronology and geography.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[GEOid]} \]

\[ \alpha_{[GEOid]} \sim Normal(0,1.5) \]

\[ \phi_{[GEOid]} \sim Exponential(1)+2 \]

8.6.1 Pigs

8.6.2 Cattle

8.6.3 Caprine

8.6.4 Edible W. Mammals

8.7 Altitude

The probability of occurrence of the most common faunal remains can be modelled against the elevation of sites in the four time periods under consideration. It is worth noting that the sites where the zooarchaeological remains have been found are not evenly distributed. In the Roman age, most sites investigated are located between 0 and 100 MSL, whereas after there is an increasing number of remains from sites between 100 and 400 MSL. Whether this reflects a real shift in settlement patterns is outside the aims of this study, but it might still be informative to visualise the different distribution of sites across elevations.

The proposed model to estimate the probability of occurrence as related to the altitude (the slope \(\beta\)) and chronology (\({[ChrID]}\)) uses a betabinomial distribution to model overdispersion in the data. The \(A\) on the left side of the formula is the outcome variable—the animal NISP counts for each observation \(i\). This is a simple intercept with slope model, where the intercept \(\alpha\) carries an index \({[ChrID]}\) as the model provides estimates for each chronology under examination. A single \(\phi\) parameter indicates the precision in the Beta distribution.

\[ A_{i} \sim BetaBinomial(NISP_{i}, \bar{p}_{i} , \phi_{i}) \]

\[ logit(\bar{p}_{i}) = \alpha_{[ChrID]} + \beta_{[ChrID]}\cdot Alt_{i} \]

\[ \alpha_{ChrID} \sim Normal(0,1.5) \]

\[ \beta_{ChrID} \sim Normal(0,1.5) \]

\[ \phi \sim Exponential(1)+2 \]

Figure 8.5: Prior predictive simulation for the altitude models used in this section.

8.7.1 Pigs

8.7.2 Cattle

8.7.3 Caprine

8.7.4 Edible W. Mammals

8.7.5 Community plot

Figure 8.6: MCMC estimates for slope and intercept plotted in the logit scale. Negative slopes indicate a negative relationship between the animal remains and increasing altitude. Intercepts were kept as a baseline occurrence probability of the species. Species on the left of the graph are rarer, species on the right are more common. It is important to notice that this represents the species response to elevation.